Dayton Children’s is attempting to better understand tumors and their different properties. We are characterizing the molecular features of cultured tumor cells grown in different micro-enviroments in order to determine how portions of a single tumor may be very different in terms of sensitivity to treatments.

These differences may help explain how a small tumor biopsy could provide the wrong information to clinicians. It could also allow researchers and clinicians to predict diagnosis and prognosis from distinct properties such as cell metabolism, which can potentially be detected by special magnetic resonance imaging techniques.

Our goal is to understand the cell growth and metabolism of a tumor through advanced imaging in order to understand how the tumor reacts to treatment inside of the body vs. in a culture. As researchers move forward with clinical trials as fast as possible, they often do not take into consideration the environmental cues that could change tumor behavior and responses to therapy.

With a mission to improve the lives of children with brain tumors, we have created a multi-institutional translational research collaboration that bridges advanced in vivo tumor imaging with in vitro studies of cell growth and metabolism.

Our goal is to understand how heterogeneity within the tumor microenvironment affects responses to treatment. Our rationale is that this information can be used to generate relevant tumor models for preclinical trials, and ultimately equip clinicians with non-invasive imaging techniques for therapeutic decision-making without the need for biopsy or other surgical intervention. Our approach uses artificial intelligence with deep learning for an unprecedented level of image analysis with correlation to clinical, biologic, and genetic data, in combination with patient-derived cell culture models.

background and significance

Pediatric brain tumors have recently surpassed leukemia as the leading cause of cancer-related death in children. Intra-tumoral heterogeneity is often a cause of treatment resistance or failure, and has remained understudied because the field has been hindered by the paucity of biological specimens, outdated culture and animal models, and erroneous extrapolation of adult data to pediatric problems.

More recent efforts for creating personalized therapeutic strategies have focused on discovering prognostic biomarkers that require high-risk, invasive tumor tissue sampling, followed by costly and rarely accessible genomic analyses. Non-invasive techniques for diagnosis, prognosis, and design of tailored therapies are not available.

Our long-term goal is to discover new tools for brain tumor management based on automated in vivo image analysis and correlation to molecular and metabolic features of experimental tumor models, for the purpose of both therapeutic decision-making as well as the construction of relevant tumor models for preclinical trials.